sequence-llm 0.1.1

A sequence-based LLM orchestration framework

Sequence-LLM

Sequence-LLM is a terminal-first CLI for running local LLMs through llama-server (llama.cpp) with profile-based model switching and automatic server lifecycle management.

It is designed for developers who run multiple local models and want a simple, reproducible workflow without writing shell scripts.

Cross-platform: Windows, Linux, macOS.

Why Sequence-LLM

Running local models often involves:

• Manually starting and stopping servers
• Remembering model paths and ports
• Managing multiple configurations
• Writing ad-hoc scripts to switch models

Sequence-LLM solves this by providing:

• Named model profiles
• Automatic start and shutdown of servers
• Interactive chat interface
• Consistent configuration across machines

Features

• Interactive CLI built with Typer and Rich
• Profile-based model switching (/brain, /coder, etc.)
• Automatic shutdown of previous server before starting a new one
• Health checking with readiness polling
• Cross-platform process management using subprocess and psutil
• OS-aware configuration directory creation
• Conversation history management
• Status panel showing active model and server info

Installation

Requirements

• Python 3.9+
• llama-server binary from llama.cpp

Install from PyPI:

pip install sequence-llm

Quick Start

Run the CLI:

pip install sequence-llm
seq-llm

On first launch, a configuration file is created automatically.

Config locations:

• Windows: %APPDATA%\sequence-llm\config.yaml
• Linux: ~/.config/sequence-llm/config.yaml
• macOS: ~/Library/Application Support/sequence-llm/config.yaml

Configuration Example

llama_server: "/path/to/llama-server"

defaults:
  threads: 6
  threads_batch: 8
  batch_size: 512

profiles:
  brain:
    name: "Brain Model"
    model_path: "/path/to/model.gguf"
    system_prompt: "/path/to/system.txt"
    port: 8081
    ctx_size: 16384
    temperature: 0.7

  coder:
    name: "Coder Model"
    model_path: "/path/to/coder.gguf"
    system_prompt: "/path/to/coder.txt"
    port: 8082
    ctx_size: 32768
    temperature: 0.3

CLI Usage

/status   → show active model and server status
/brain    → switch to brain profile
/coder    → switch to coder profile
/clear    → clear conversation history
/quit     → stop server and exit

Typing any text sends a message to the active model.

Example Workflow

1. Start CLI
2. Automatically load default model
3. Switch between models using commands
4. Chat interactively without restarting processes manually

Architecture

Core components:

• CLI: interactive interface and command routing
• Server Manager: lifecycle control of llama-server
• API Client: communication with local inference server
• Config System: YAML-based profiles and defaults

Development

Clone repository:

git clone https://github.com/Ananay28425/Sequence-LLM.git
cd Sequence-LLM
pip install -e .

Run tests:

pytest -v

License

MIT License. See LICENSE file for details.

Contributing

Pull requests and issues are welcome.

GitHub: https://github.com/Ananay28425/Sequence-LLM

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Sequence-LLM provides a lightweight and predictable way to manage local LLM workflows from the terminal.

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Sequence-LLM - Orchestrate LLM sequences with ease.